KGA Proposals Task Force Survey
Kindly complete the survey below to express your interest in participating in various consortia for upcoming Horizon Europe calls.
For each call in the survey, scroll down or click here for detailed information about the call.
If you have any questions, please contact us at proposals@kg-alliance.org.
Summary of the current calls
Advancing Large AI Models: Integration of New Data Modalities and Expansion of Capabilities (AI, Data and Robotics Partnership) (RIA)
Deadline: 18 September 2024 (Single stage)
Budget: around 25000000
# of proposals to be funded: 2
ExpectedOutcome:
Projects are expected to contribute to one or more of the following outcomes:
Enhanced applicability of large AI systems to new domains through the integration of innovative data modalities, such as sensor measurements (e.g. in robotics, IoT) or remote sensing (e.g. earth observation), as input.
Improvement of current multimodal large AI systems capabilities and expansion of the number of data modalities jointly handed by one AI system, leading to broader application potential and improved AI performance.
Scope:
Large artificial intelligence (AI) models refer to a new generation of general-purpose AI models (i.e., generative AI) capable of adapting to diverse domains and tasks without significant modification. Notable examples, such as OpenAI’s GPT-4V and META’s Llama 2 or DinoV2, have demonstrated a wide and growing variety of capabilities.
The swift progression of large AI models in recent years holds immense potential to revolutionize various industries, due to their ability to adapt to diverse tasks and domains. For them to achieve their potential, access to vast data repositories, significant computing resources, and skilled engineers is required. A promising avenue of research is the development of multi-modal large AI models that can seamlessly integrate multiple modalities, including text, structured data, computer code, visual or audio media, robotics or IoT sensors, and remote sensing data.
This topic centres around the development of innovative multimodal large AI models, covering both the training of foundation models and their subsequent fine-tuning. These models should show superior capabilities across a wide array of down-stream tasks. The emphasis is both on integrating new input data modalities into large AI models and on developing multimodal large AI models with either significantly higher capabilities and/or the ability to handle a greater number of modalities.
Moreover, projects should contribute to reinforcing Europe’s research excellence in the field of large AI models by driving substantial scientific progress and innovation in key large AI areas. This includes the development of novel methods for pretraining multimodal foundation models. Additionally, novel approaches to effective and efficient fine-tuning of such models should be pursued.
Research activities should explore innovative methodologies for enhancing the representation, alignment, and interaction among the different data modalities, thereby substantially improving the overall performance and trustworthiness of these models. Advances in efficient computation for the pre-training, execution and fine-tuning of foundation models to reduce their computational and environmental impact, and increasing the safety of models are also topics of interest.
Proposals should outline how the models will incorporate trustworthiness, considering factors such as explainability, security, and privacy in line with provisions in the upcoming Artificial Intelligence Act. Additionally, the models should incorporate characteristics that align with European values, and provide improved multilingual capabilities, where relevant.
Proposals should address at least one of the following focus areas:
the integration of innovative modalities of data for large AI models during training and inference. Examples of innovative modalities include event streams, structured data and sensor measurements. The incorporation of such new modalities could potentially bring unforeseen enhancements to model performance and enable their application in new domains like weather forecasting, robotics, and manufacturing.
enhanced multimodal models that exceed the current state of the art, with either significantly improved capabilities or the ability to handle a larger number of modalities. This focus area also encompasses models capable of multi-modal output generation. Current large-scale multimodal models most commonly engage with only vision and language.
Each proposal is expected to address all of the following:
Data Collection, Processing and Cross-modal Alignment. The proposal should describe convincingly the characteristics and availability of the large, trustworthy data sources, as well as the trustworthy data processing to be utilised within the project, detailing the data processing steps to ensure reliability, accountability and transparency, and the alignment of data among the different modalities. A modest portion (up to 10%) of the budget may be allocated to data collection activities; proposals may involve relevant data owners in this task, if necessary. Importantly, the proposal should delineate how potential privacy and IPR issues associated with the data will be managed and mitigated.
Multimodal Foundation Model Pretraining. The pretrained multimodal foundation model is expected to demonstrate high capabilities across a wide range of tasks. The pretraining tasks used should be agnostic of down-stream tasks. These activities also cover the development of the codebase and implementation of small-scale experiments. A minor portion (up to 10%) of the budget may be allocated for the acquisition of computing resources for codebase development and small-scale experiments, though the primary source of computing resources for pretraining should be sought from external high-performance computing facilities such as EuroHPC or National centres. The proposal should describe convincingly the strategy to access these computing resources.
Fine-Tuning of Multimodal Foundation Models: The proposal should clearly detail the activities pursued to fine-tune the model for diverse downstream tasks demonstrating illustrative potential use-cases. The tasks’ output may either be of a single modality or multimodality. Research activities should investigate innovative methodologies designed to bolster the interplay between different data modalities, thereby enhancing the overall performance of these models.
Testing and Evaluation: The proposal should detail the development of workflows, benchmarks, testing procedures, and pertinent tools for evaluating both foundation and fine-tuned models. Attention should be paid to the performance, transparency, bias, robustness, accuracy, and security of the models, through appropriate testing procedures (e.g., red teaming for safety and security), in compliance with the future AI Act.
Proposals should adopt a multidisciplinary research team, as appropriate, to cover all the above issues.
Proposals should adhere to Horizon Europe’s guidelines regarding Open Science practices as well as the FAIR data principles. Open access should be provided to research outputs – including training datasets, software tools, model architecture and hyperparameters, as well as model weights – unless a legitimate interest or constraint applies. Additionally, proposals are encouraged to deliver results under open-source licenses.
All proposals are expected to embed mechanisms to assess and demonstrate progress (with qualitative and quantitative KPIs, benchmarking and progress monitoring, including participation to international evaluation contests, as well as illustrative application use-cases demonstrating concrete potential added value), and share communicable results with the European R&D community, through the AI-on-demand platform, and Common European data spaces, and if necessary other relevant digital resource platforms in order to enhance the European AI, Data and Robotics ecosystem through the sharing of results and best practice.
Proposals are also expected to dedicate tasks and resources to collaborate with and provide input to the open innovation challenge under HORIZON-CL4-2023-HUMAN-01-04. Research teams involved in the proposals are expected to participate in the respective Innovation Challenges.
This topic implements the co-programmed European Partnership on AI, data and robotics.
Specific Topic Conditions:
Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project.
Explainable and Robust AI (AI Data and Robotics Partnership) (RIA)
Deadline: 18 September 2024 (Single stage)
Budget: around 7500000
# of proposals to be funded: 2
ExpectedOutcome:
Projects are expected to contribute to one of the following outcomes:
- Enhanced robustness, performance and reliability of AI systems, including generative AI models, with awareness of the limits of operational robustness of the system.
- Improved explainability and accountability, transparency and autonomy of AI systems, including generative AI models, along with an awareness of the working conditions of the system.
Scope:
Trustworthy AI solutions, need to be robust, safe and reliable when operating in real-world conditions, and need to be able to provide adequate, meaningful and complete explanations when relevant, or insights into causality, account for concerns about fairness, be robust when dealing with such issues in real world conditions, while aligned with rights and obligations around the use of AI systems in Europe. Advances across these areas can help create human-centric AI[1], which reflects the needs and values of European citizens and contribute to an effective governance of AI technologies.
The need for transparent and robust AI systems has become more pressing with the rapid growth and commercialisation of generative AI systems based on foundation models. Despite their impressive capabilities, trustworthiness remains an unresolved, fundamental scientific challenge. Due to the intricate nature of generative AI systems, understanding or explaining the rationale behind their outputs is normally not possible with current explainable AI methods. Moreover, these models occasionally tend to ‘hallucinate’, generating non-factual or inaccurate information, further compromising their reliability.
To achieve robust and reliable AI, novel approaches are needed to develop methods and solutions that work under other than model-ideal circumstances, while also having an awareness when these conditions break down. To achieve trustworthiness, AI system should be sufficiently transparent and capable of explaining how the system has reached a conclusion in a way that it is meaningful to the user, enabling safe and secure human-machine interaction, while also indicating when the limits of operation have been reached.
The purpose is to advance AI-algorithms and innovations based on them that can perform safely under a common variety of circumstances, reliably in real-world conditions and predict when these operational circumstances are no longer valid. The research should aim at advancing robustness and explainability for a generality of solutions, while leading to an acceptable loss in accuracy and efficiency, and with known verifiability and reproducibility. The focus is on extending the general applicability of explainability and robustness of AI-systems by foundational AI and machine learning research. To this end, the following methods may be considered but are not necessarily restricted to:
- data-efficient learning, transformers and alternative architectures, self-supervised learning, fine-tuning of foundation models, reinforcement learning, federated and edge-learning, automated machine learning, or any combination thereof for improved robustness and explainability.
- hybrid approaches integrating learning, knowledge and reasoning, neurosymbolic methods, model-based approaches, neuromorphic computing, or other nature-inspired approaches and other forms of hybrid combinations which are generically applicable to robustness and explainability.
- continual learning, active learning, long-term learning and how they can help improve robustness and explainability.
- multi-modal learning, natural language processing, speech recognition and text understanding taking multicultural aspects into account for the purpose of increased operational robustness and the capability to explain alternative formulation[2].
Multidisciplinary research activities should address all of the following:
- Proposals should involve appropriate expertise in all the relevant sector specific use cases and disciplines, and where appropriate Social Sciences and Humanities (SSH), including gender and intersectional knowledge to address concerns around gender, racial or other biases, etc.
- Proposals are expected to dedicate tasks and resources to collaborate with and provide input to the open innovation challenge under HORIZON-CL4-2023-HUMAN-01-04 addressing explainability and robustness. Research teams involved in the proposals are expected to participate in the respective Innovation Challenges.
- Contribute to making AI and robotics solutions meet the requirements of Trustworthy AI, based on the respect of the ethical principles, the fundamental rights including critical aspects such as robustness, safety, reliability, in line with the European Approach to AI. Ethics principles needs to be adopted from early stages of development and design.
All proposals are expected to embed mechanisms to assess and demonstrate progress (with qualitative and quantitative KPIs, benchmarking and progress monitoring), and share communicable results with the European R&D community, through the AI-on-demand platform or Digital Industrial Platform for Robotics, public community resources, to maximise re-use of results, either by developers, or for uptake, and optimise efficiency of funding; enhancing the European AI, Data and Robotics ecosystem and possible sector-specific forums through the sharing of results and best practice.
In order to achieve the expected outcomes, international cooperation is encouraged, in particular with Canada and India.
Specific Topic Conditions:
Activities are expected to start at TRL 2-3 and achieve TRL 4-5 by the end of the project.
[1]A European approach to artificial intelligence | Shaping Europe’s digital future (europa.eu)
[2]Research should complement build upon and collaborate with projects funded under topic HORIZON-CL4-2023-HUMAN-01-03: Natural Language Understanding and Interaction in Advanced Language Technologies
Digital Humanism – Putting people at the centre of the digital transformation (CSA)
Deadline: 18 September 2024 (Single stage)
Budget: around 1500000
# of proposals to be funded: 1
Expected outcome:
- Create an active network and cross disciplinary communities on digital humanism bringing together ICT experts, ethnologists, sociologists and experts in fundamental rights
- Help defining and strengthening EU’s approach to a human-centred digital transformation through cross-disciplinary, world class foundational and application oriented research
- Formulate approaches how to transform and strengthen European standards (rule of law, social market economy, fundamental rights, social standards and social partnership) into the digital realm including the take up of those standards by digital actors and in particular those developing new digital environment (e.g. data scientists, start-ups, investors)
- Formulate a list of recommendations and roadmaps to address current gaps or issues that are preventing the development of digital solutions that will reinforce – and not undermine – digital humanism across the society
- Propose a concrete framework for measuring and promoting progress of the promotion and putting into practice of the digital rights and principles declaration in the context of the Digital Decade policy programme. This will include concrete indicators, source of information at national and European level, as well as the identification of existing, and development of new, capacity tools to support the uptake of identified best practice uses of digital technologies in support of digital humanism. This work will notably feed the review of the Digital Decade Policy Programme and of the solemn declaration in 2026.
Scope:
A horizontal and holistic approach is needed for creating a more resilient, inclusive and democratic European society, prepared and responsive to opportunities, societal changes, threats and disasters, addressing inequalities and providing protection and high-quality public services such as health care, and empowering all citizens to act in the green and digital transitions.
While digital technologies bring strong advantages coming along with a promise of freedom and innovation, negative aspects have also become visible in the last years. These include the monopolization of the information space, increasing levels of fake news and disinformation, strong power of online platforms, cyber threats and crimes, privacy breaches, strong market disparities as well as an economic order that claims human experience as free raw material for commercial practices of extraction, prediction and sales (Zuboff, 2019).
Digital Humanism is here defined as the continuing search for a European answer to keep up high civilization standards stemming from enlightenment and the humanist era, and to further develop them in the digital world. In line with European values, such a digital environment should enable all Europeans to make full use of digital and technologies, to have a society where geographical distance matters less, so that all Europeans can benefit from the digitalisation in their daily activities (ranging from work, learning, to enjoying culture or leisure activities) but also in their interactions with governments, and participation in democratic processes.
This requires intense, cross-disciplinary work of computer (and technology) sciences with legal, economic, sociological, philosophical and other kinds of expertise as a co-development exercise. To support in-depth, early-on collaboration between computer sciences and the whole wealth of humanities and social sciences to put new algorithms and models into a broader context, proposals under this topic should:
- Support the development of cross-disciplinary communities and networks in relation to digital transformation of society. It is thus critical to foster greater exchanges between social sciences and technological communities.
- Support the cross-disciplinary co-development of new theoretical models and approaches of the impact of digital technologies in our societies, starting with human and societal needs.
- Showcasing success stories and examples of engagement of the digital community seeking to develop concrete ways to progress toward a more human-centred digital world and draw concrete conclusions from these.
- Mapping out collaborative research to develop concrete tools and frameworks for ensuring that all actors of the European digital ecosystem (policy makers, business, startup developers, investors, NGOs) can integrate in their work and activities the values that form a human centred digital transformation and develop a roadmap for the possible development of research activities
- Develop a conceptual framework as well as tools and indicators to monitor and promote the progress of the ‘declaration on digital rights and principles’ and its six chapters (putting people at the centre of the digital transformation; solidarity and inclusion; freedom of choice; participation in the digital public space; safety, security and empowerment; sustainability), notably to feed the review of the Digital Decade Policy Programme and of the solemn declaration in 2026.
This project is also relevant in the policy context of the Digital Decade policy programme (“The Path to the Digital Decade”), which sets a European approach for its digital transformation based on values and technological leadership. For the first time, societal and human centred objectives are fully integrated into a comprehensive governance mechanism at EU level including monitoring of the progress made by the digital transformation in reaching our collective values and quantitative digital targets (skills, infrastructures, digitalisation of business and public services).
Facilitate the engagement in global ICT standardisation development (CSA)
Deadline: 18 September 2024 (Single stage)
Budget: around 6000000
# of proposals to be funded: 1
Expected Outcome:
Share information about global sectorial ICT standardisation ecosystems and engagement of European stakeholders in global standardisation settings.
Projects are expected to contribute to the following outcomes:
- Set-up of a facility to support participation of European specialists in international ICT Standard Developing Organisations (SDOs) and global fora and consortia, which should increase the influence of Europe into ICT standardisation, including representation in leadership and key positions, to promote incorporation of European requirements, values and interests in ICT standardisation;
- Develop and update sectorial ICT standardisation landscape and gap analysis of ICT standardisation needs in support of EU policies as outlined in the Rolling Plan for ICT standardisation;
- Cooperate, synchronise and achieve capacity building with other similar initiatives or European players including from EU (and national) funded R&I projects; provide a forum for foresight analysis in different sectors;
- Increase awareness on ICT standardisation development;
- Financially support standardisation meetings in Europe of international SDOs and global fora and consortia, so that European players have easier conditions for participation.
Scope:
This action will contribute to the objectives spelled out in the EU Standardisation Strategy and meeting the objectives of the European Green Deal and Europe’s Digital Decade, in particular to supporting the EU’s leading position in global standards-setting as a forerunner in key technologies and promoting EU core values,by supporting and empowering the participation of European stakeholders in the development of open technical specifications and standards with the aim to strengthen European competitiveness and sovereignty, promoting EU values and ethics, and strengthen the take-up, scalability and cross-sector interoperability of their technological solutions. This action will among the others support the Commission’s effort to address the critical issues related to internet, trusted and secured chips, or data standards as described in the EU Standardisation Strategy.
The aim is to reinforce the presence of EU and associated states experts in the global ICT standardisation scene, by setting up an ICT standardisation observatory and a facility supporting the participation of key European specialists (especially from SMEs, societal stakeholders and Academia) in key international and global Standard Developing Organisations[1]. In particular, the project should foresee actions related to topics in the Rolling Plan for ICT standardisation as well as related to internet standardisation.
The action will also contribute to the objective of promoting EU cutting-edge innovation that fosters timely standards, by coordinating with other EU funded projects and action that may contribute with their results to ICT standardisation, as well as with EU supported PPPs and Joint Undertakings, seeking for synergies.
To achieve these objectives, proposals under this topic should provide for:
- Landscape and gap analysis of international ICT standardisation, including identification of sectors and areas, in particular within the field of internet standardisation, quantum network, IoT, 6G mobile communication, data, edge computing, artificial intelligence, eGovernment, block chain / DLT, cyber security, smart cities & communities, data centres, trusted chips, robotics, circular economy certification etc.
- Setting up of a management facility to support contributions and leadership (e.g. chairing of technical committees, convenor positions) of European specialists (incl. from SMEs and academia) in activities in relation to international standardisation including in global ICT SDOs, fora and consortia.
- When relevant, support financially the hosting standardisation meetings and workshops in Europe to ease the participation of European experts;
- Facilitation of a foresight committee, which liaises with relevant on-going developments in EU and national Member States funded R&I projects, in particular with projects having identified standardisation outputs or with potential relevant results to contribute to standardisation, including as well other coordination and support actions, and relevant European Partnerships;
- Promotion of the relevance and benefits of ICT standardisation, especially for European industry competitiveness, driving sustainability, sovereignty, supporting objectives of the European green deal and EU values and ethics. The proposal will also include actions, including development of tools and materials, to promote education on ICT standardisation;
The proposal should take into account the previous activities carried out the observatory and facilities for funding experts within the topics ICT-40-2017 implemented by the StandICT.eu project and ICT-45-2020 implemented under StandICT.eu2023 project (see http://www.standict.eu).
In order to achieve the expected outcomes, international cooperation is strongly encouraged.
In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement.
[1]Such us ISO, IEC, ISO/IEC JTC1, ITU-T, 3GPP, IETF, OneM2M, W3C, OASIS, IEEE